Time series RNN

Data Loading

# Download the file
url <- "https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip"
download.file(url, destfile = "jena_climate_2009_2016.csv.zip")

# Unzip the file
unzip("jena_climate_2009_2016.csv.zip", exdir = ".")

# Load the unzipped CSV file
climate_data <- read.csv("jena_climate_2009_2016.csv")

# Display the first few rows of the data
head(climate_data)

##             Date.Time p..mbar. T..degC. Tpot..K. Tdew..degC. rh....
## 1 01.01.2009 00:10:00   996.52    -8.02   265.40       -8.90   93.3
## 2 01.01.2009 00:20:00   996.57    -8.41   265.01       -9.28   93.4
## 3 01.01.2009 00:30:00   996.53    -8.51   264.91       -9.31   93.9
## 4 01.01.2009 00:40:00   996.51    -8.31   265.12       -9.07   94.2
## 5 01.01.2009 00:50:00   996.51    -8.27   265.15       -9.04   94.1
## 6 01.01.2009 01:00:00   996.50    -8.05   265.38       -8.78   94.4
##   VPmax..mbar. VPact..mbar. VPdef..mbar. sh..g.kg. H2OC..mmol.mol. rho..g.m..3.
## 1         3.33         3.11         0.22      1.94            3.12      1307.75
## 2         3.23         3.02         0.21      1.89            3.03      1309.80
## 3         3.21         3.01         0.20      1.88            3.02      1310.24
## 4         3.26         3.07         0.19      1.92            3.08      1309.19
## 5         3.27         3.08         0.19      1.92            3.09      1309.00
## 6         3.33         3.14         0.19      1.96            3.15      1307.86
##   wv..m.s. max..wv..m.s. wd..deg.
## 1     1.03          1.75    152.3
## 2     0.72          1.50    136.1
## 3     0.19          0.63    171.6
## 4     0.34          0.50    198.0
## 5     0.32          0.63    214.3
## 6     0.21          0.63    192.7

Library Loading

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(keras)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(tensorflow)

Data Exploration and Pre-processing

# View the structure and summary of the data
str(climate_data)

## 'data.frame':    420451 obs. of  15 variables:
##  $ Date.Time      : chr  "01.01.2009 00:10:00" "01.01.2009 00:20:00" "01.01.2009 00:30:00" "01.01.2009 00:40:00" ...
##  $ p..mbar.       : num  997 997 997 997 997 ...
##  $ T..degC.       : num  -8.02 -8.41 -8.51 -8.31 -8.27 -8.05 -7.62 -7.62 -7.91 -8.43 ...
##  $ Tpot..K.       : num  265 265 265 265 265 ...
##  $ Tdew..degC.    : num  -8.9 -9.28 -9.31 -9.07 -9.04 -8.78 -8.3 -8.36 -8.73 -9.34 ...
##  $ rh....         : num  93.3 93.4 93.9 94.2 94.1 94.4 94.8 94.4 93.8 93.1 ...
##  $ VPmax..mbar.   : num  3.33 3.23 3.21 3.26 3.27 3.33 3.44 3.44 3.36 3.23 ...
##  $ VPact..mbar.   : num  3.11 3.02 3.01 3.07 3.08 3.14 3.26 3.25 3.15 3 ...
##  $ VPdef..mbar.   : num  0.22 0.21 0.2 0.19 0.19 0.19 0.18 0.19 0.21 0.22 ...
##  $ sh..g.kg.      : num  1.94 1.89 1.88 1.92 1.92 1.96 2.04 2.03 1.97 1.88 ...
##  $ H2OC..mmol.mol.: num  3.12 3.03 3.02 3.08 3.09 3.15 3.27 3.26 3.16 3.02 ...
##  $ rho..g.m..3.   : num  1308 1310 1310 1309 1309 ...
##  $ wv..m.s.       : num  1.03 0.72 0.19 0.34 0.32 0.21 0.18 0.19 0.28 0.59 ...
##  $ max..wv..m.s.  : num  1.75 1.5 0.63 0.5 0.63 0.63 0.63 0.5 0.75 0.88 ...
##  $ wd..deg.       : num  152 136 172 198 214 ...

summary(climate_data)

##   Date.Time            p..mbar.         T..degC.          Tpot..K.    
##  Length:420451      Min.   : 913.6   Min.   :-23.010   Min.   :250.6  
##  Class :character   1st Qu.: 984.2   1st Qu.:  3.360   1st Qu.:277.4  
##  Mode  :character   Median : 989.6   Median :  9.410   Median :283.5  
##                     Mean   : 989.2   Mean   :  9.449   Mean   :283.5  
##                     3rd Qu.: 994.7   3rd Qu.: 15.470   3rd Qu.:289.5  
##                     Max.   :1015.4   Max.   : 37.280   Max.   :311.3  
##   Tdew..degC.          rh....        VPmax..mbar.    VPact..mbar.   
##  Min.   :-25.010   Min.   : 12.95   Min.   : 0.95   Min.   : 0.790  
##  1st Qu.:  0.240   1st Qu.: 65.21   1st Qu.: 7.78   1st Qu.: 6.210  
##  Median :  5.210   Median : 79.30   Median :11.82   Median : 8.860  
##  Mean   :  4.954   Mean   : 76.01   Mean   :13.58   Mean   : 9.533  
##  3rd Qu.: 10.070   3rd Qu.: 89.40   3rd Qu.:17.60   3rd Qu.:12.350  
##  Max.   : 23.110   Max.   :100.00   Max.   :63.77   Max.   :28.320  
##   VPdef..mbar.      sh..g.kg.      H2OC..mmol.mol.   rho..g.m..3. 
##  Min.   : 0.000   Min.   : 0.500   Min.   : 0.800   Min.   :1059  
##  1st Qu.: 0.870   1st Qu.: 3.920   1st Qu.: 6.290   1st Qu.:1187  
##  Median : 2.190   Median : 5.590   Median : 8.960   Median :1214  
##  Mean   : 4.042   Mean   : 6.022   Mean   : 9.639   Mean   :1216  
##  3rd Qu.: 5.300   3rd Qu.: 7.800   3rd Qu.:12.480   3rd Qu.:1243  
##  Max.   :46.010   Max.   :18.130   Max.   :28.820   Max.   :1394  
##     wv..m.s.     max..wv..m.s.       wd..deg.    
##  Min.   : 0.00   Min.   : 0.000   Min.   :  0.0  
##  1st Qu.: 0.99   1st Qu.: 1.760   1st Qu.:124.8  
##  Median : 1.76   Median : 2.960   Median :198.1  
##  Mean   : 2.13   Mean   : 3.532   Mean   :174.7  
##  3rd Qu.: 2.86   3rd Qu.: 4.740   3rd Qu.:234.1  
##  Max.   :14.63   Max.   :23.500   Max.   :360.0

# Display the first few rows of the dataset
head(climate_data)

##             Date.Time p..mbar. T..degC. Tpot..K. Tdew..degC. rh....
## 1 01.01.2009 00:10:00   996.52    -8.02   265.40       -8.90   93.3
## 2 01.01.2009 00:20:00   996.57    -8.41   265.01       -9.28   93.4
## 3 01.01.2009 00:30:00   996.53    -8.51   264.91       -9.31   93.9
## 4 01.01.2009 00:40:00   996.51    -8.31   265.12       -9.07   94.2
## 5 01.01.2009 00:50:00   996.51    -8.27   265.15       -9.04   94.1
## 6 01.01.2009 01:00:00   996.50    -8.05   265.38       -8.78   94.4
##   VPmax..mbar. VPact..mbar. VPdef..mbar. sh..g.kg. H2OC..mmol.mol. rho..g.m..3.
## 1         3.33         3.11         0.22      1.94            3.12      1307.75
## 2         3.23         3.02         0.21      1.89            3.03      1309.80
## 3         3.21         3.01         0.20      1.88            3.02      1310.24
## 4         3.26         3.07         0.19      1.92            3.08      1309.19
## 5         3.27         3.08         0.19      1.92            3.09      1309.00
## 6         3.33         3.14         0.19      1.96            3.15      1307.86
##   wv..m.s. max..wv..m.s. wd..deg.
## 1     1.03          1.75    152.3
## 2     0.72          1.50    136.1
## 3     0.19          0.63    171.6
## 4     0.34          0.50    198.0
## 5     0.32          0.63    214.3
## 6     0.21          0.63    192.7

# Use the dmy_hms() function from 'lubridate' to parse the date-time
climate_data$Date.Time <- dmy_hms(climate_data$Date.Time)

# Check if the conversion was successful
head(climate_data$Date.Time)

## [1] "2009-01-01 00:10:00 UTC" "2009-01-01 00:20:00 UTC"
## [3] "2009-01-01 00:30:00 UTC" "2009-01-01 00:40:00 UTC"
## [5] "2009-01-01 00:50:00 UTC" "2009-01-01 01:00:00 UTC"

# Select relevant features: Temperature, Pressure, and Humidity
# Adjust the feature selection as needed for your model
climate_data <- climate_data %>%
  select(Date.Time, T..degC., p..mbar., rh....)

# Rename the columns for easier access
colnames(climate_data) <- c("DateTime", "Temperature", "Pressure", "Humidity")

# Plot Temperature over time
ggplot(climate_data, aes(x = DateTime, y = Temperature)) +
  geom_line(color = "blue") +
  labs(title = "Temperature Over Time", x = "Date Time", y = "Temperature (°C)") +
  theme_minimal()

# Plot Pressure over time
ggplot(climate_data, aes(x = DateTime, y = Pressure)) +
  geom_line(color = "red") +
  labs(title = "Pressure Over Time", x = "Date Time", y = "Pressure (mbar)") +
  theme_minimal()

# Plot Humidity over time
ggplot(climate_data, aes(x = DateTime, y = Humidity)) +
  geom_line(color = "green") +
  labs(title = "Humidity Over Time", x = "Date Time", y = "Humidity (%)") +
  theme_minimal()

Data Preparation

create_sequences <- function(data, seq_length) {
  X <- array(0, dim = c(nrow(data) - seq_length, seq_length, ncol(data) - 1))
  y <- numeric(nrow(data) - seq_length)
  
  for (i in 1:(nrow(data) - seq_length)) {
    X[i, , ] <- as.matrix(data[i:(i + seq_length - 1), -1])  # Exclude Date.Time
    y[i] <- data[i + seq_length, 2]  # Assuming we're predicting the second column (e.g., Temperature)
  }
  
  list(X = X, y = y)
}

# Define the sequence length
sequence_length <- 10

# Create sequences
sequences <- create_sequences(climate_data, sequence_length)

# Split the data into training and testing sets (80-20 split)
train_size <- floor(0.8 * length(sequences$y))
X_train <- sequences$X[1:train_size, , ]
y_train <- sequences$y[1:train_size]
X_test <- sequences$X[(train_size + 1):length(sequences$y), , ]
y_test <- sequences$y[(train_size + 1):length(sequences$y)]



# Display the dimensions
cat("Training Set Dimensions (X_train): ", dim(X_train), "\n")

## Training Set Dimensions (X_train):  336352 10 3

cat("Training Set Dimensions (y_train): ", length(y_train), "\n")

## Training Set Dimensions (y_train):  336352

cat("Testing Set Dimensions (X_test): ", dim(X_test), "\n")

## Testing Set Dimensions (X_test):  84089 10 3

cat("Testing Set Dimensions (y_test): ", length(y_test), "\n")

## Testing Set Dimensions (y_test):  84089

RNN model

# Define the RNN model
model <- keras_model_sequential() %>%
  layer_simple_rnn(units = 50, input_shape = c(sequence_length, 3), return_sequences = TRUE) %>%
  layer_simple_rnn(units = 25) %>%
  layer_dense(units = 1)

# Compile the model
model %>% compile(
  optimizer = "adam",
  loss = "mean_squared_error",
  metrics = c("mean_absolute_error")
)

# Fit the model
history <- model %>% fit(
  x = X_train,
  y = y_train,
  batch_size = 32,  # Use a smaller batch size to conserve memory
  epochs = 20,      # Adjust the number of epochs as needed
  validation_split = 0.2,
  verbose = 1
)

## Epoch 1/20
## 
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## 8409/8409 [==============================] - 61s 7ms/step - loss: 76.1766 - mean_absolute_error: 7.1142 - val_loss: 53.1368 - val_mean_absolute_error: 5.9986
## Epoch 2/20
## 
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## 8409/8409 [==============================] - 57s 7ms/step - loss: 75.9510 - mean_absolute_error: 7.1050 - val_loss: 52.9442 - val_mean_absolute_error: 5.9913
## Epoch 3/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9589 - mean_absolute_error: 7.1054 - val_loss: 52.8528 - val_mean_absolute_error: 5.9879
## Epoch 4/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9555 - mean_absolute_error: 7.1050 - val_loss: 52.8953 - val_mean_absolute_error: 5.9894
## Epoch 5/20
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9578 - mean_absolute_error: 7.1051 - val_loss: 53.4255 - val_mean_absolute_error: 6.0097
## Epoch 6/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9527 - mean_absolute_error: 7.1049 - val_loss: 52.5213 - val_mean_absolute_error: 5.9754
## Epoch 7/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9568 - mean_absolute_error: 7.1052 - val_loss: 52.2942 - val_mean_absolute_error: 5.9671
## Epoch 8/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9573 - mean_absolute_error: 7.1052 - val_loss: 53.1878 - val_mean_absolute_error: 6.0006
## Epoch 9/20
## 
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## 8409/8409 [==============================] - ETA: 0s - loss: 75.9559 - mean_absolute_error: 7.1047
## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9559 - mean_absolute_error: 7.1047 - val_loss: 52.8061 - val_mean_absolute_error: 5.9861
## Epoch 10/20
## 
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## 8409/8409 [==============================] - 59s 7ms/step - loss: 75.9567 - mean_absolute_error: 7.1051 - val_loss: 52.2254 - val_mean_absolute_error: 5.9646
## Epoch 11/20
## 
##    1/8409 [..............................] - ETA: 0s - loss: 99.9115 - mean_absolute_error: 8.3821
##   10/8409 [..............................] - ETA: 1:04 - loss: 77.7881 - mean_absolute_error: 7.1860
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## 8409/8409 [==============================] - ETA: 0s - loss: 75.9556 - mean_absolute_error: 7.1051
## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9556 - mean_absolute_error: 7.1051 - val_loss: 53.2447 - val_mean_absolute_error: 6.0028
## Epoch 12/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9535 - mean_absolute_error: 7.1049 - val_loss: 53.2176 - val_mean_absolute_error: 6.0017
## Epoch 13/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9575 - mean_absolute_error: 7.1053 - val_loss: 53.2066 - val_mean_absolute_error: 6.0013
## Epoch 14/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9518 - mean_absolute_error: 7.1047 - val_loss: 52.9602 - val_mean_absolute_error: 5.9919
## Epoch 15/20
## 
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## 8409/8409 [==============================] - 60s 7ms/step - loss: 75.9559 - mean_absolute_error: 7.1051 - val_loss: 52.7364 - val_mean_absolute_error: 5.9835
## Epoch 16/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9528 - mean_absolute_error: 7.1049 - val_loss: 52.9169 - val_mean_absolute_error: 5.9903
## Epoch 17/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9526 - mean_absolute_error: 7.1050 - val_loss: 52.8926 - val_mean_absolute_error: 5.9894
## Epoch 18/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9524 - mean_absolute_error: 7.1052 - val_loss: 52.5255 - val_mean_absolute_error: 5.9756
## Epoch 19/20
## 
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## 8409/8409 [==============================] - 58s 7ms/step - loss: 75.9506 - mean_absolute_error: 7.1047 - val_loss: 52.9491 - val_mean_absolute_error: 5.9915
## Epoch 20/20
## 
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## 8409/8409 [==============================] - 59s 7ms/step - loss: 75.9521 - mean_absolute_error: 7.1047 - val_loss: 52.1896 - val_mean_absolute_error: 5.9633

# Evaluate the model on the test set
model %>% evaluate(X_test, y_test)

## 2628/2628 - 7s - loss: 70.7799 - mean_absolute_error: 6.8369 - 7s/epoch - 3ms/step

##                loss mean_absolute_error 
##            70.77995             6.83686

# Make predictions
predictions <- model %>% predict(X_test)

## 2628/2628 - 7s - 7s/epoch - 3ms/step

# Plot the true vs. predicted values
plot(y_test, predictions, main = "True vs Predicted", xlab = "True Values", ylab = "Predicted Values", col = "blue", pch = 20)

Task 1 Adjusted layers for RNN

# Define the RNN model with adjustable units
model <- keras_model_sequential() %>%
  layer_simple_rnn(units = 100, return_sequences = TRUE, input_shape = c(sequence_length, 3)) %>%  # First layer with 100 units
  layer_simple_rnn(units = 50, return_sequences = TRUE) %>%  # Second layer with 50 units
  layer_simple_rnn(units = 25) %>%  # Third layer with 25 units
  layer_dense(units = 1)  # Output layer

# Compile the model
model %>% compile(
  optimizer = "adam",
  loss = "mean_squared_error",
  metrics = c("mean_absolute_error")
)

The first layer_simple_rnn is configured to take the sequences with a shape defined by sequence_length (number of time steps) and n_features (number of input features). Here, units = 100 can be adjusted to fit your performance needs. The subsequent RNN layers can have different numbers of units, with return_sequences = TRUE for the first two layers, allowing the RNN to return the full sequence of outputs. The final RNN layer should not have return_sequences, as it will output only the last hidden state. A dense layer is used for the final prediction, which can be adjusted to match your output needs (in this case, predicting a single value).

# Fit the model to the training data
history <- model %>% fit(
  x = X_train,
  y = y_train,
  batch_size = 32,  # Adjust batch size as needed
  epochs = 10,      # Set the number of epochs
  validation_split = 0.2,
  verbose = 1
)

## Epoch 1/10
## 
##    1/8409 [..............................] - ETA: 8:26:37 - loss: 131.0662 - mean_absolute_error: 9.2369
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## 8409/8409 [==============================] - 82s 9ms/step - loss: 76.1024 - mean_absolute_error: 7.1113 - val_loss: 52.7319 - val_mean_absolute_error: 5.9833
## Epoch 2/10
## 
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## 8409/8409 [==============================] - 79s 9ms/step - loss: 75.9528 - mean_absolute_error: 7.1050 - val_loss: 52.6023 - val_mean_absolute_error: 5.9785
## Epoch 3/10
## 
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## 8409/8409 [==============================] - 79s 9ms/step - loss: 75.9553 - mean_absolute_error: 7.1053 - val_loss: 52.3473 - val_mean_absolute_error: 5.9690
## Epoch 4/10
## 
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## 8409/8409 [==============================] - 83s 10ms/step - loss: 75.9557 - mean_absolute_error: 7.1053 - val_loss: 52.9037 - val_mean_absolute_error: 5.9898
## Epoch 5/10
## 
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## 8409/8409 [==============================] - ETA: 0s - loss: 75.9574 - mean_absolute_error: 7.1053
## 8409/8409 [==============================] - 79s 9ms/step - loss: 75.9574 - mean_absolute_error: 7.1053 - val_loss: 53.1702 - val_mean_absolute_error: 5.9999
## Epoch 6/10
## 
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## 8409/8409 [==============================] - 79s 9ms/step - loss: 75.9529 - mean_absolute_error: 7.1049 - val_loss: 52.8707 - val_mean_absolute_error: 5.9885
## Epoch 7/10
## 
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## 8409/8409 [==============================] - 244s 29ms/step - loss: 75.9534 - mean_absolute_error: 7.1053 - val_loss: 53.0165 - val_mean_absolute_error: 5.9940
## Epoch 8/10
## 
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## 8409/8409 [==============================] - 79s 9ms/step - loss: 75.9551 - mean_absolute_error: 7.1049 - val_loss: 53.3564 - val_mean_absolute_error: 6.0071
## Epoch 9/10
## 
##    1/8409 [..............................] - ETA: 3:07 - loss: 65.0788 - mean_absolute_error: 6.6057
##    9/8409 [..............................] - ETA: 1:08 - loss: 79.3795 - mean_absolute_error: 7.1586
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## 8409/8409 [==============================] - 79s 9ms/step - loss: 75.9545 - mean_absolute_error: 7.1051 - val_loss: 52.7711 - val_mean_absolute_error: 5.9848
## Epoch 10/10
## 
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## 8409/8409 [==============================] - 79s 9ms/step - loss: 75.9541 - mean_absolute_error: 7.1049 - val_loss: 53.1123 - val_mean_absolute_error: 5.9977

# Evaluate the model
model_performance <- model %>% evaluate(X_test, y_test)

## 2628/2628 - 10s - loss: 72.7332 - mean_absolute_error: 6.9122 - 10s/epoch - 4ms/step

# Make predictions
predictions <- model %>% predict(X_test)

## 2628/2628 - 10s - 10s/epoch - 4ms/step

# Plot the true vs. predicted values
plot(y_test, predictions, main = "True vs Predicted", xlab = "True Values", ylab = "Predicted Values", col = "blue", pch = 20)

Task 2 Using layer_lstm() instead of layer_gru()

# Define the LSTM model with adjustable units
model <- keras_model_sequential() %>%
  layer_lstm(units = 100, return_sequences = TRUE, input_shape = c(sequence_length, 3)) %>%  # First LSTM layer
  layer_lstm(units = 50, return_sequences = TRUE) %>%  # Second LSTM layer
  layer_lstm(units = 25) %>%  # Third LSTM layer
  layer_dense(units = 1)  # Output layer


# Compile the model
model %>% compile(
  optimizer = "adam",
  loss = "mean_squared_error",
  metrics = c("mean_absolute_error")
)

# Fit the model to the training data
history <- model %>% fit(
  x = X_train,
  y = y_train,
  batch_size = 32,  # Adjust batch size as needed
  epochs = 10,      # Set the number of epochs
  validation_split = 0.2,
  verbose = 1
)

## Epoch 1/10
## 
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## 8409/8409 [==============================] - 172s 20ms/step - loss: 4.5260 - mean_absolute_error: 0.9590 - val_loss: 0.1235 - val_mean_absolute_error: 0.2415
## Epoch 2/10
## 
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## 8409/8409 [==============================] - 164s 19ms/step - loss: 0.2329 - mean_absolute_error: 0.3506 - val_loss: 0.2674 - val_mean_absolute_error: 0.4367
## Epoch 3/10
## 
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## 8409/8409 [==============================] - 164s 20ms/step - loss: 0.1715 - mean_absolute_error: 0.3018 - val_loss: 0.1554 - val_mean_absolute_error: 0.3154
## Epoch 4/10
## 
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## 8409/8409 [==============================] - 164s 19ms/step - loss: 0.1532 - mean_absolute_error: 0.2883 - val_loss: 0.1996 - val_mean_absolute_error: 0.3834
## Epoch 5/10
## 
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## 8409/8409 [==============================] - 164s 20ms/step - loss: 0.1410 - mean_absolute_error: 0.2757 - val_loss: 0.0576 - val_mean_absolute_error: 0.1654
## Epoch 6/10
## 
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## 8409/8409 [==============================] - 164s 20ms/step - loss: 0.1623 - mean_absolute_error: 0.2898 - val_loss: 0.1337 - val_mean_absolute_error: 0.3028
## Epoch 7/10
## 
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## 8409/8409 [==============================] - ETA: 0s - loss: 0.1416 - mean_absolute_error: 0.2775
## 8409/8409 [==============================] - 165s 20ms/step - loss: 0.1416 - mean_absolute_error: 0.2775 - val_loss: 0.2150 - val_mean_absolute_error: 0.4205
## Epoch 8/10
## 
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## 8409/8409 [==============================] - 164s 19ms/step - loss: 0.1353 - mean_absolute_error: 0.2712 - val_loss: 0.0763 - val_mean_absolute_error: 0.2172
## Epoch 9/10
## 
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## 8409/8409 [==============================] - ETA: 0s - loss: 0.1273 - mean_absolute_error: 0.2628
## 8409/8409 [==============================] - 165s 20ms/step - loss: 0.1273 - mean_absolute_error: 0.2628 - val_loss: 0.1936 - val_mean_absolute_error: 0.3965
## Epoch 10/10
## 
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## 8409/8409 [==============================] - 301s 36ms/step - loss: 0.1291 - mean_absolute_error: 0.2650 - val_loss: 0.0702 - val_mean_absolute_error: 0.1820

# Evaluate the model
model_performance <- model %>% evaluate(X_test, y_test)

## 2628/2628 - 19s - loss: 0.0940 - mean_absolute_error: 0.2027 - 19s/epoch - 7ms/step

# Make predictions
predictions <- model %>% predict(X_test)

## 2628/2628 - 20s - 20s/epoch - 8ms/step

# Plot the true vs. predicted values
plot(y_test, predictions, main = "True vs Predicted", xlab = "True Values", ylab = "Predicted Values", col = "blue", pch = 20)

Using LSTM layers can help in capturing complex patterns in your time series data more effectively than GRUs, especially for longer sequences.

Task 3 Using a combination of 1d connvents and RNN

# Define the combined CNN-LSTM model without reshaping
model <- keras_model_sequential() %>%
  layer_conv_1d(filters = 64, kernel_size = 3, activation = 'relu', input_shape = c(sequence_length, 3)) %>%
  layer_max_pooling_1d(pool_size = 2) %>%
  layer_conv_1d(filters = 128, kernel_size = 3, activation = 'relu') %>%
  layer_max_pooling_1d(pool_size = 2) %>%
  layer_lstm(units = 50, return_sequences = TRUE) %>%
  layer_lstm(units = 25) %>%
  layer_dense(units = 1)  # Output layer



# Compile the model
model %>% compile(
  optimizer = "adam",
  loss = "mean_squared_error",
  metrics = c("mean_absolute_error")
)

# Print the model summary to verify shapes
summary(model)

## Model: "sequential_3"
## ________________________________________________________________________________
##  Layer (type)                       Output Shape                    Param #     
## ================================================================================
##  conv1d_1 (Conv1D)                  (None, 8, 64)                   640         
##  max_pooling1d_1 (MaxPooling1D)     (None, 4, 64)                   0           
##  conv1d (Conv1D)                    (None, 2, 128)                  24704       
##  max_pooling1d (MaxPooling1D)       (None, 1, 128)                  0           
##  lstm_4 (LSTM)                      (None, 1, 50)                   35800       
##  lstm_3 (LSTM)                      (None, 25)                      7600        
##  dense_3 (Dense)                    (None, 1)                       26          
## ================================================================================
## Total params: 68770 (268.63 KB)
## Trainable params: 68770 (268.63 KB)
## Non-trainable params: 0 (0.00 Byte)
## ________________________________________________________________________________

# Fit the model to the training data
history <- model %>% fit(
  x = X_train,
  y = y_train,
  batch_size = 32,  # Adjust batch size as needed
  epochs = 10,      # Set the number of epochs
  validation_split = 0.2,
  verbose = 1
)

## Epoch 1/10
## 
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## 8409/8409 [==============================] - 76s 8ms/step - loss: 76.5275 - mean_absolute_error: 7.1282 - val_loss: 52.9990 - val_mean_absolute_error: 5.9934
## Epoch 2/10
## 
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## 8409/8409 [==============================] - 70s 8ms/step - loss: 75.9468 - mean_absolute_error: 7.1044 - val_loss: 53.2993 - val_mean_absolute_error: 6.0048
## Epoch 3/10
## 
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## 8409/8409 [==============================] - 70s 8ms/step - loss: 75.9496 - mean_absolute_error: 7.1048 - val_loss: 52.8510 - val_mean_absolute_error: 5.9878
## Epoch 4/10
## 
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## 8409/8409 [==============================] - 69s 8ms/step - loss: 75.9464 - mean_absolute_error: 7.1048 - val_loss: 52.2403 - val_mean_absolute_error: 5.9651
## Epoch 5/10
## 
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## 8409/8409 [==============================] - 75s 9ms/step - loss: 75.9528 - mean_absolute_error: 7.1051 - val_loss: 52.8032 - val_mean_absolute_error: 5.9860
## Epoch 6/10
## 
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## 8409/8409 [==============================] - 70s 8ms/step - loss: 75.9477 - mean_absolute_error: 7.1048 - val_loss: 52.7597 - val_mean_absolute_error: 5.9844
## Epoch 7/10
## 
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## 8409/8409 [==============================] - 70s 8ms/step - loss: 75.9489 - mean_absolute_error: 7.1049 - val_loss: 52.7326 - val_mean_absolute_error: 5.9833
## Epoch 8/10
## 
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## 8409/8409 [==============================] - 70s 8ms/step - loss: 75.9488 - mean_absolute_error: 7.1047 - val_loss: 52.5775 - val_mean_absolute_error: 5.9775
## Epoch 9/10
## 
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## 8409/8409 [==============================] - ETA: 0s - loss: 75.9471 - mean_absolute_error: 7.1045
## 8409/8409 [==============================] - 70s 8ms/step - loss: 75.9471 - mean_absolute_error: 7.1045 - val_loss: 52.5259 - val_mean_absolute_error: 5.9756
## Epoch 10/10
## 
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## 8409/8409 [==============================] - 70s 8ms/step - loss: 75.9525 - mean_absolute_error: 7.1050 - val_loss: 53.1455 - val_mean_absolute_error: 5.9989

# Evaluate the model
model_performance <- model %>% evaluate(X_test, y_test)

## 2628/2628 - 9s - loss: 72.7980 - mean_absolute_error: 6.9147 - 9s/epoch - 3ms/step

# Make predictions
predictions <- model %>% predict(X_test)

## 2628/2628 - 10s - 10s/epoch - 4ms/step

# Plot the true vs. predicted values
plot(y_test, predictions, main = "True vs Predicted", xlab = "True Values", ylab = "Predicted Values", col = "blue", pch = 20)